Es una prueba de analisis de los datos obtenidos por el laboratorio y datos sensoriales.
Los datos corresponden al análisis de calidad y datos sensoriales
data_cofqual %>%
dimnames()
[[1]]
[1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10"
[[2]]
[1] "Variedad" "Proteina" "Sacarosa"
[4] "Acido_Clorogenico" "Grasas" "Cafeina"
coffee_quality_1 = coffee_quality%>%
select(-ID)%>%
column_to_rownames(var = "Variedad")
stars(coffee_quality_1[, 1:5], key.loc = c(10, 2.1),
draw.segments = TRUE, col.segments = hcl.colors(9, "Set 2"))
data_Sensory_1 = data_sensory%>%
column_to_rownames(var = "Variedad")
stars(data_Sensory_1[, 1:5], key.loc = c(10, 2.1),
draw.segments = TRUE, col.segments = hcl.colors(9, "Set 2"))
#join data
data_pca = left_join(data_sensory, data_cofqual, by = "Variedad") %>%
#convertir la primera columna en nombre de filas para pca
column_to_rownames(var = "Variedad") %>%
select(-Uniformidad, -Taza_Limpia)
str(data_pca)
'data.frame': 10 obs. of 12 variables:
$ Sabor : num 8 7.75 8 7.75 8 8 8 7.75 7.75 8
$ Retrogusto : num 7.5 7.5 7.75 7.75 8 7.75 7.75 7.75 7.5 8
$ Acidez : num 7.75 8 7.75 7.75 7.75 8 8 7.75 8 7.75
$ Cuerpo : num 8 7.75 7.75 7.5 7.75 7.75 8 7.75 7.75 7.75
$ Balance : num 7.75 7.75 8 7.5 7.75 7.75 8 7.75 7.75 7.75
$ Dulzura : num 8 7.75 8 8 8 8.5 8.5 8 8 8
$ General : num 10 8 8 8 10 10 10 10 10 10
$ Proteina : num 13.8 12.9 13 13.9 12.6 ...
$ Sacarosa : num 4.02 4.07 4.35 4.74 4.89 4.15 4.1 4.22 4.4 4.21
$ Acido_Clorogenico: num 328 220 223 324 281 ...
$ Grasas : num 2.2 2.22 1.55 2.81 2.02 1.27 2.48 2.23 2.45 2.31
$ Cafeina : num 334 280 328 339 284 ...
pca_data_active = data_pca[, 1:12]
res.pca <- PCA(pca_data_active, scale.unit=TRUE, ncp=6, graph = FALSE)
plot(res.pca, label = "none")
# Extraer valores propios/varianzas
get_eig(res.pca)
eigenvalue variance.percent cumulative.variance.percent
Dim.1 4.0257149 33.5476243 33.54762
Dim.2 2.3933109 19.9442574 53.49188
Dim.3 1.5636474 13.0303948 66.52228
Dim.4 1.4315627 11.9296888 78.45197
Dim.5 1.1656073 9.7133944 88.16536
Dim.6 0.5434554 4.5287953 92.69415
Dim.7 0.4549817 3.7915144 96.48567
Dim.8 0.3027852 2.5232096 99.00888
Dim.9 0.1189345 0.9911211 100.00000
# Visualizar valores propios/variaciones
fviz_screeplot(res.pca, addlabels = TRUE, ylim = c(0, 50))
# Control variable colors using their contributions
fviz_pca_var(res.pca, col.var="contrib",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE # Avoid text overlapping
)
# Contributions of variables to PC1
fviz_contrib(res.pca, choice = "var", axes = 1, top = 10)
# Contributions of variables to PC2
fviz_contrib(res.pca, choice = "var", axes = 2, top = 10)
# Biplot of individuals and variables
fviz_pca_biplot(res.pca, repel = TRUE)
coffee_sensory_1 = coffee_sensory%>%
na.omit() %>%
select(Variedad, where(is.numeric), -Catador, -ID)%>%
group_by(Variedad) %>%
summarise(across(2:10, .fns = mean)) %>%
ungroup()%>%
column_to_rownames(var = "Variedad")
pca_data_active1 = coffee_sensory_1[, 1:9]
res.pca <- PCA(pca_data_active1, scale.unit=TRUE, ncp=9, graph = FALSE)
# Extraer valores propios/varianzas
get_eig(res.pca)
eigenvalue variance.percent cumulative.variance.percent
Dim.1 3.159502971 35.1055886 35.10559
Dim.2 2.150633122 23.8959236 59.00151
Dim.3 1.117777274 12.4197475 71.42126
Dim.4 1.040622759 11.5624751 82.98373
Dim.5 0.802317374 8.9146375 91.89837
Dim.6 0.569135631 6.3237292 98.22210
Dim.7 0.108022421 1.2002491 99.42235
Dim.8 0.042227316 0.4691924 99.89154
Dim.9 0.009761131 0.1084570 100.00000
# Visualizar valores propios/variaciones
fviz_screeplot(res.pca, addlabels = TRUE, ylim = c(0, 50))
# Control variable colors using their contributions
fviz_pca_var(res.pca, col.var="contrib",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE # Avoid text overlapping
)
# Contributions of variables to PC1
fviz_contrib(res.pca, choice = "var", axes = 1, top = 10)
# Contributions of variables to PC2
fviz_contrib(res.pca, choice = "var", axes = 2, top = 10)
# Biplot of individuals and variables
fviz_pca_biplot(res.pca, repel = TRUE)